On the basis of analyzing the classical algorithms of network clustering and community detecting, a new algorithmic method of discovery of community was brought up, which has different thinking from previous algorithms. In this method a general network is looked upon as a corresponding electrical network firstly, the link in between the connected pairs of nodes is substituted by a fixed resisto...

Our modern society has come to depend on large scale infrastructure networks to deliver resources to our homes and businesses in an efficient manner. Over the past 10 years there have been numerous examples where a local disturbance has led the global failure of the system. In this paper we use an evolutionary algorithm to evolve complex networks that are resilient to cascading failure. We then...

A new dynamical clustering algorithm for the identification of modules in complex networks has been recently introduced [1]. In this paper we present a modified version of this algorithm based on a system of chaotic Rössler oscillators and we test its sensitivity on real and computer generated networks with a well known modular structure.

Simulation modeling of complex systems is receiving increasing research attention over the past years. In this paper, we discuss the basic concepts involved in multi-resolution simulation modeling of complex stochastic systems. We argue that, in many cases, using the average over all available high-resolution simulation results as the input to subsequent low-resolution modules is inappropriate ...

It is a well-known problem of remotely sensed images classification due to its complexity. This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditional K-means clustering algorithm. First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing imag...

We investigate the properties of the returns of the main emerging stock markets from Europe by means of complex networks. We transform the series of daily returns into complex networks, and analyze the local properties of these networks with respect to degree distributions, clustering, or average line length. We further use the clustering coefficients as quantities describing the local structur...

Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, we devise a novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG’s Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-mean...

Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the g...

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...

MOTIVATION Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters and complex regulatory mechanisms underlying biological syste...